1
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Yao K, Schaafsma E, Zhang B, Cheng C. Tumor cell intrinsic and extrinsic features predict prognosis in estrogen receptor positive breast cancer. PLoS Comput Biol 2022; 18:e1009495. [PMID: 35263321 PMCID: PMC8936467 DOI: 10.1371/journal.pcbi.1009495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 03/21/2022] [Accepted: 02/17/2022] [Indexed: 11/19/2022] Open
Abstract
Although estrogen-receptor-positive (ER+) breast cancer is generally associated with favorable prognosis, clinical outcome varies substantially among patients. Genomic assays have been developed and applied to predict patient prognosis for personalized treatment. We hypothesize that the recurrence risk of ER+ breast cancer patients is determined by both genomic mutations intrinsic to tumor cells and extrinsic immunological features in the tumor microenvironment. Based on the Cancer Genome Atlas (TCGA) breast cancer data, we identified the 72 most common genomic aberrations (including gene mutations and indels) in ER+ breast cancer and defined sample-specific scores that systematically characterized the deregulated pathways intrinsic to tumor cells. To further consider tumor cell extrinsic features, we calculated immune infiltration scores for six major immune cell types. Many individual intrinsic features are predictive of patient prognosis in ER+ breast cancer, and some of them achieved comparable accuracy with the Oncotype DX assay. In addition, statistical learning models that integrated these features predicts the recurrence risk of patients with significantly better performance than the Oncotype DX assay (our optimized random forest model AUC = 0.841, Oncotype DX model AUC = 0.792, p = 0.04). As a proof-of-concept, our study indicates the great potential of genomic and immunological features in prognostic prediction for improving breast cancer precision medicine. The framework introduced in this work can be readily applied to other cancers.
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Affiliation(s)
- Kevin Yao
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas, United States of America
| | - Evelien Schaafsma
- Department of Molecular and Systems Biology, Dartmouth College, Lebanon, New Hampshire, United States of America
- Department of Biomedical Data Science, The Geisel School of Medicine at Dartmouth College, Lebanon, New Hampshire, United States of America
| | - Baoyi Zhang
- Department of Chemical and Biomolecular Engineering, Rice University, Houston, Texas, United States of America
| | - Chao Cheng
- Department of Medicine, Baylor College of Medicine, Houston, Texas, United States of America
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas, United States of America
- Institute for Clinical and Transcriptional Research, Baylor College of Medicine, Houston, Texas, United States of America
- * E-mail:
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2
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A framework to predict the applicability of Oncotype DX, MammaPrint, and E2F4 gene signatures for improving breast cancer prognostic prediction. Sci Rep 2022; 12:2211. [PMID: 35140308 PMCID: PMC8828770 DOI: 10.1038/s41598-022-06230-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Accepted: 01/18/2022] [Indexed: 11/08/2022] Open
Abstract
To improve cancer precision medicine, prognostic and predictive biomarkers are critically needed to aid physicians in deciding treatment strategies in a personalized fashion. Due to the heterogeneous nature of cancer, most biomarkers are expected to be valid only in a subset of patients. Furthermore, there is no current approach to determine the applicability of biomarkers. In this study, we propose a framework to improve the clinical application of biomarkers. As part of this framework, we develop a clinical outcome prediction model (CPM) and a predictability prediction model (PPM) for each biomarker and use these models to calculate a prognostic score (P-score) and a confidence score (C-score) for each patient. Each biomarker’s P-score indicates its association with patient clinical outcomes, while each C-score reflects the biomarker applicability of the biomarker’s CPM to a patient and therefore the confidence of the clinical prediction. We assessed the effectiveness of this framework by applying it to three biomarkers, Oncotype DX, MammaPrint, and an E2F4 signature, which have been used for predicting patient response, pathologic complete response versus residual disease to neoadjuvant chemotherapy (a classification problem), and recurrence-free survival (a Cox regression problem) in breast cancer, respectively. In both applications, our analyses indicated patients with higher C scores were more likely to be correctly predicted by the biomarkers, indicating the effectiveness of our framework. This framework provides a useful approach to develop and apply biomarkers in the context of cancer precision medicine.
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3
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Wang X, Han Y, Peng J, He J. CCR5 is a prognostic biomarker and an immune regulator for triple negative breast cancer. Aging (Albany NY) 2021; 13:23810-23830. [PMID: 34717291 PMCID: PMC8580338 DOI: 10.18632/aging.203654] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 09/18/2021] [Indexed: 12/24/2022]
Abstract
This study aims to explore the clinical implications and potential mechanistic functions of CCR5 in triple negative breast cancer. Briefly, we demonstrated that CCR5 is overexpressed in TNBC and is associated with better prognosis of TNBC. CCR5 expression is positively correlated with tumor immune cell infiltration and tumor immune response related pathways. Multi-omics data analyses identified CCR5 associated genomic and proteomic changes. CCR5 overexpression was associated with better overall survival in TNBC patients with TP53 mutation. We also summarized the latest findings on ICB efficacy related genes and explored the association between CCR5 and those genes. These results indicated that CCR5 is a potential tumor suppressor gene and individualized therapeutic strategy could be established based on multi-omics background and expression pattern of ICB related genes. In conclusion, CCR5 is associated with better survival of TNBC patients with TP53 mutation, which may exert its roles through tumor immune environment.
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Affiliation(s)
- Xin Wang
- Thoracic Surgery Department, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yong Han
- Department of Thoracic Surgery, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China.,Key Laboratory of Tumor Molecular Diagnosis and Individualized Medicine of Zhejiang Province, Zhejiang, China
| | - Jiamin Peng
- Department of Clinical Laboratory, Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang 310012, China
| | - Jie He
- Thoracic Surgery Department, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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4
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Hickey TE, Selth LA, Chia KM, Laven-Law G, Milioli HH, Roden D, Jindal S, Hui M, Finlay-Schultz J, Ebrahimie E, Birrell SN, Stelloo S, Iggo R, Alexandrou S, Caldon CE, Abdel-Fatah TM, Ellis IO, Zwart W, Palmieri C, Sartorius CA, Swarbrick A, Lim E, Carroll JS, Tilley WD. The androgen receptor is a tumor suppressor in estrogen receptor-positive breast cancer. Nat Med 2021; 27:310-320. [PMID: 33462444 DOI: 10.1038/s41591-020-01168-7] [Citation(s) in RCA: 116] [Impact Index Per Article: 38.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Accepted: 11/03/2020] [Indexed: 01/28/2023]
Abstract
The role of the androgen receptor (AR) in estrogen receptor (ER)-α-positive breast cancer is controversial, constraining implementation of AR-directed therapies. Using a diverse, clinically relevant panel of cell-line and patient-derived models, we demonstrate that AR activation, not suppression, exerts potent antitumor activity in multiple disease contexts, including resistance to standard-of-care ER and CDK4/6 inhibitors. Notably, AR agonists combined with standard-of-care agents enhanced therapeutic responses. Mechanistically, agonist activation of AR altered the genomic distribution of ER and essential co-activators (p300, SRC-3), resulting in repression of ER-regulated cell cycle genes and upregulation of AR target genes, including known tumor suppressors. A gene signature of AR activity positively predicted disease survival in multiple clinical ER-positive breast cancer cohorts. These findings provide unambiguous evidence that AR has a tumor suppressor role in ER-positive breast cancer and support AR agonism as the optimal AR-directed treatment strategy, revealing a rational therapeutic opportunity.
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Affiliation(s)
- Theresa E Hickey
- Dame Roma Mitchell Cancer Research Laboratories, Adelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia
| | - Luke A Selth
- Dame Roma Mitchell Cancer Research Laboratories, Adelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia
- Flinders Health and Medical Research Institute, Flinders University, Adelaide, South Australia, Australia
- Freemason's Foundation Centre for Men's Health, University of Adelaide, Adelaide, South Australia, Australia
| | - Kee Ming Chia
- Garvan Institute of Medical Research & St Vincent's Clinical School, University of New South Wales, Sydney, New South Wales, Australia
| | - Geraldine Laven-Law
- Dame Roma Mitchell Cancer Research Laboratories, Adelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia
| | - Heloisa H Milioli
- Garvan Institute of Medical Research & St Vincent's Clinical School, University of New South Wales, Sydney, New South Wales, Australia
| | - Daniel Roden
- Garvan Institute of Medical Research & St Vincent's Clinical School, University of New South Wales, Sydney, New South Wales, Australia
| | - Shalini Jindal
- Dame Roma Mitchell Cancer Research Laboratories, Adelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia
| | - Mun Hui
- Garvan Institute of Medical Research & St Vincent's Clinical School, University of New South Wales, Sydney, New South Wales, Australia
| | | | - Esmaeil Ebrahimie
- Dame Roma Mitchell Cancer Research Laboratories, Adelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia
| | - Stephen N Birrell
- Dame Roma Mitchell Cancer Research Laboratories, Adelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia
| | - Suzan Stelloo
- Oncode Institute, Netherlands Cancer Institute, Amsterdam, the Netherlands
- Oncode Institute, Radboud University Nijmegen, Nijmegen, the Netherlands
| | - Richard Iggo
- Dame Roma Mitchell Cancer Research Laboratories, Adelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia
- Institut Bergonié, University of Bordeaux, Bordeaux, France
| | - Sarah Alexandrou
- Garvan Institute of Medical Research & St Vincent's Clinical School, University of New South Wales, Sydney, New South Wales, Australia
| | - C Elizabeth Caldon
- Garvan Institute of Medical Research & St Vincent's Clinical School, University of New South Wales, Sydney, New South Wales, Australia
| | | | | | - Wilbert Zwart
- Oncode Institute, Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Carlo Palmieri
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool & Clatterbridge Centre NHS Foundation Trust, Liverpool, UK
| | | | - Alex Swarbrick
- Garvan Institute of Medical Research & St Vincent's Clinical School, University of New South Wales, Sydney, New South Wales, Australia
| | - Elgene Lim
- Garvan Institute of Medical Research & St Vincent's Clinical School, University of New South Wales, Sydney, New South Wales, Australia
| | - Jason S Carroll
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Wayne D Tilley
- Dame Roma Mitchell Cancer Research Laboratories, Adelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia.
- Freemason's Foundation Centre for Men's Health, University of Adelaide, Adelaide, South Australia, Australia.
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5
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Voutsadakis IA. Clinical Implications of Chromosomal Instability (CIN) and Kinetochore Abnormalities in Breast Cancers. Mol Diagn Ther 2020; 23:707-721. [PMID: 31372940 DOI: 10.1007/s40291-019-00420-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Genetic instability is a defining property of cancer cells and is the basis of various lesions including point mutations, copy number alterations and translocations. Chromosomal instability (CIN) is part of the genetic instability of cancer and consists of copy number alterations in whole or parts of cancer cell chromosomes. CIN is observed in differing degrees in most cancers. In breast cancer, CIN is commonly part of the genomic landscape of the disease and has a higher incidence in aggressive sub-types. Tumor suppressors that are commonly mutated or disabled in cancer, such as p53 and pRB, play roles in protection against CIN, and as a result, their dysfunction contributes to the establishment or tolerance of CIN. Several structural and regulatory proteins of the centromeres and kinetochore, the complex structure that is responsible for the correct distribution of genetic material in the daughter cells during mitosis, are direct or, mostly, indirect transcription targets of p53 and pRB. Thus, despite the absence of structural defects in genes encoding for centromere and kinetochore components, dysfunction of these tumor suppressors may have profound implications for the correct function of the mitotic apparatus contributing to CIN. CIN and its prognostic and therapeutic implications in breast cancer are discussed in this article.
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Affiliation(s)
- Ioannis A Voutsadakis
- Algoma District Cancer Program, Sault Area Hospital, 750 Great Northern Road, Sault Ste Marie, ON, P6B 0A8, Canada. .,Section of Internal Medicine, Division of Clinical Sciences, Northern Ontario School of Medicine, Sudbury, ON, Canada.
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6
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Linares A, Assou S, Lapierre M, Thouennon E, Duraffourd C, Fromaget C, Boulahtouf A, Tian G, Ji J, Sahin O, Badia E, Boulle N, Cavaillès V. Increased expression of the HDAC9 gene is associated with antiestrogen resistance of breast cancers. Mol Oncol 2019; 13:1534-1547. [PMID: 31099456 PMCID: PMC6599838 DOI: 10.1002/1878-0261.12505] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2018] [Revised: 04/26/2019] [Accepted: 05/15/2019] [Indexed: 12/19/2022] Open
Abstract
Estrogens play a pivotal role in breast cancer etiology, and endocrine therapy remains the main first line treatment for estrogen receptor‐alpha (ERα)‐positive breast cancer. ER are transcription factors whose activity is finely regulated by various regulatory complexes, including histone deacetylases (HDACs). Here, we investigated the role of HDAC9 in ERα signaling and response to antiestrogens in breast cancer cells. Various Michigan Cancer Foundation‐7 (MCF7) breast cancer cell lines that overexpress class IIa HDAC9 or that are resistant to the partial antiestrogen 4‐hydroxy‐tamoxifen (OHTam) were used to study phenotypic changes in response to ER ligands by using transcriptomic and gene set enrichment analyses. Kaplan–Meier survival analyses were performed using public transcriptomic datasets from human breast cancer biopsies. In MCF7 breast cancer cells, HDAC9 decreased ERα mRNA and protein expression and inhibited its transcriptional activity. Conversely, HDAC9 mRNA was strongly overexpressed in OHTam‐resistant MCF7 cells and in ERα‐negative breast tumor cell lines. Moreover, HDAC9‐overexpressing cells were less sensitive to OHTam antiproliferative effects compared with parental MCF7 cells. Several genes (including MUC1, SMC3 and S100P) were similarly deregulated in OHTam‐resistant and in HDAC9‐overexpressing MCF7 cells. Finally, HDAC9 expression was positively associated with genes upregulated in endocrine therapy‐resistant breast cancers and high HDAC9 levels were associated with worse prognosis in patients treated with OHTam. These results demonstrate the complex interactions of class IIa HDAC9 with ERα signaling in breast cancer cells and its effect on the response to hormone therapy.
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Affiliation(s)
- Aurélien Linares
- IRCM, Institut de Recherche en Cancérologie de Montpellier, France.,INSERM, U1194, Montpellier, France.,Université Montpellier, France.,ICM, Montpellier, France
| | - Said Assou
- Université Montpellier, France.,IRMB, Institute for Regenerative Medicine & Biotherapy, Montpellier, France.,INSERM, U1183, Montpellier, France
| | - Marion Lapierre
- IRCM, Institut de Recherche en Cancérologie de Montpellier, France.,INSERM, U1194, Montpellier, France.,Université Montpellier, France.,ICM, Montpellier, France
| | - Erwan Thouennon
- IRCM, Institut de Recherche en Cancérologie de Montpellier, France.,INSERM, U1194, Montpellier, France.,Université Montpellier, France.,ICM, Montpellier, France
| | - Céline Duraffourd
- Laboratoire de Biopathologie des Tumeurs, CHU Arnaud de Villeneuve, Montpellier, France
| | - Carole Fromaget
- Laboratoire de Biopathologie des Tumeurs, CHU Arnaud de Villeneuve, Montpellier, France
| | - Abdelhay Boulahtouf
- IRCM, Institut de Recherche en Cancérologie de Montpellier, France.,INSERM, U1194, Montpellier, France.,Université Montpellier, France.,ICM, Montpellier, France
| | - Gao Tian
- Key Laboratory of Carcinogenesis and Translational Research Ministry of Education, Department of Gastrointestinal Surgery, Peking University Cancer Hospital & Institute, Beijing, China
| | - Jiafu Ji
- Key Laboratory of Carcinogenesis and Translational Research Ministry of Education, Department of Gastrointestinal Surgery, Peking University Cancer Hospital & Institute, Beijing, China
| | - Ozgur Sahin
- Department of Drug Discovery and Biomedical Sciences, University of South Carolina, Columbia, SC, USA
| | - Eric Badia
- IRCM, Institut de Recherche en Cancérologie de Montpellier, France.,INSERM, U1194, Montpellier, France.,Université Montpellier, France.,ICM, Montpellier, France
| | - Nathalie Boulle
- IRCM, Institut de Recherche en Cancérologie de Montpellier, France.,INSERM, U1194, Montpellier, France.,Université Montpellier, France.,ICM, Montpellier, France.,Laboratoire de Biopathologie des Tumeurs, CHU Arnaud de Villeneuve, Montpellier, France
| | - Vincent Cavaillès
- IRCM, Institut de Recherche en Cancérologie de Montpellier, France.,INSERM, U1194, Montpellier, France.,Université Montpellier, France.,ICM, Montpellier, France
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7
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Hergueta-Redondo M, Sarrio D, Molina-Crespo Á, Vicario R, Bernadó-Morales C, Martínez L, Rojo-Sebastián A, Serra-Musach J, Mota A, Martínez-Ramírez Á, Castilla MÁ, González-Martin A, Pernas S, Cano A, Cortes J, Nuciforo PG, Peg V, Palacios J, Pujana MÁ, Arribas J, Moreno-Bueno G. Gasdermin B expression predicts poor clinical outcome in HER2-positive breast cancer. Oncotarget 2018; 7:56295-56308. [PMID: 27462779 PMCID: PMC5302915 DOI: 10.18632/oncotarget.10787] [Citation(s) in RCA: 72] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2015] [Accepted: 07/06/2016] [Indexed: 01/03/2023] Open
Abstract
Around, 30–40% of HER2-positive breast cancers do not show substantial clinical benefit from the targeted therapy and, thus, the mechanisms underlying resistance remain partially unknown. Interestingly, ERBB2 is frequently co-amplified and co-expressed with neighbour genes that may play a relevant role in this cancer subtype. Here, using an in silico analysis of data from 2,096 breast tumours, we reveal a significant correlation between Gasdermin B (GSDMB) gene (located 175 kilo bases distal from ERBB2) expression and the pathological and clinical parameters of poor prognosis in HER2-positive breast cancer. Next, the analysis of three independent cohorts (totalizing 286 tumours) showed that approximately 65% of the HER2-positive cases have GSDMB gene amplification and protein over-expression. Moreover, GSDMB expression was also linked to poor therapeutic responses in terms of lower relapse free survival and pathologic complete response as well as positive lymph node status and the development of distant metastasis under neoadjuvant and adjuvant treatment settings, respectively. Importantly, GSDMB expression promotes survival to trastuzumab in different HER2-positive breast carcinoma cells, and is associated with trastuzumab resistance phenotype in vivo in Patient Derived Xenografts. In summary, our data identifies the ERBB2 co-amplified and co-expressed gene GSDMB as a critical determinant of poor prognosis and therapeutic response in HER2-positive breast cancer.
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Affiliation(s)
- Marta Hergueta-Redondo
- Biochemistry Department, Universidad Autónoma de Madrid (UAM), Instituto de Investigaciones Biomédicas "Alberto Sols" (CSIC-UAM), IdiPAZ, Madrid, Spain
| | - David Sarrio
- Biochemistry Department, Universidad Autónoma de Madrid (UAM), Instituto de Investigaciones Biomédicas "Alberto Sols" (CSIC-UAM), IdiPAZ, Madrid, Spain
| | - Ángela Molina-Crespo
- Biochemistry Department, Universidad Autónoma de Madrid (UAM), Instituto de Investigaciones Biomédicas "Alberto Sols" (CSIC-UAM), IdiPAZ, Madrid, Spain
| | - Rocío Vicario
- Preclinical Oncology Program, Vall d'Hebron Institute of Oncology (VHIO), Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Cristina Bernadó-Morales
- Preclinical Oncology Program, Vall d'Hebron Institute of Oncology (VHIO), Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Lidia Martínez
- Biochemistry Department, Universidad Autónoma de Madrid (UAM), Instituto de Investigaciones Biomédicas "Alberto Sols" (CSIC-UAM), IdiPAZ, Madrid, Spain
| | | | - Jordi Serra-Musach
- Breast Cancer and Systems Biology Unit, ProCURE, Catalan Institute of Oncology, IDIBELL, L'Hospitalet del Llobregat, Barcelona, Spain
| | - Alba Mota
- Biochemistry Department, Universidad Autónoma de Madrid (UAM), Instituto de Investigaciones Biomédicas "Alberto Sols" (CSIC-UAM), IdiPAZ, Madrid, Spain.,Translational Research Laboratory, MD Anderson Internacional Foundation, Madrid, Spain
| | | | - Mª Ángeles Castilla
- Pathology Department, Hospital Universitario Virgen del Rocío, Sevilla, Spain
| | | | - Sonia Pernas
- Breast Cancer and Systems Biology Unit, ProCURE, Catalan Institute of Oncology, IDIBELL, L'Hospitalet del Llobregat, Barcelona, Spain
| | - Amparo Cano
- Biochemistry Department, Universidad Autónoma de Madrid (UAM), Instituto de Investigaciones Biomédicas "Alberto Sols" (CSIC-UAM), IdiPAZ, Madrid, Spain
| | - Javier Cortes
- Clinical Oncology Program, Vall d'Hebron Institute of Oncology (VHIO), Universitat Autonoma de Barcelona, Barcelona, Spain.,Oncology Department, Hospital Universitario Ramón y Cajal, Madrid, Spain
| | - Paolo G Nuciforo
- Molecular Oncology Program, Vall d'Hebron Institute of Oncology (VHIO), Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Vicente Peg
- Pathology Department, Hospital Vall d'Hebron University, Barcelona, Spain
| | - José Palacios
- Pathology Department, Hospital Universitario Virgen del Rocío, Sevilla, Spain.,Pathology Department, Hospital Universitario Ramón y Cajal, Madrid, Spain
| | - Miguel Ángel Pujana
- Breast Cancer and Systems Biology Unit, ProCURE, Catalan Institute of Oncology, IDIBELL, L'Hospitalet del Llobregat, Barcelona, Spain
| | - Joaquín Arribas
- Preclinical Oncology Program, Vall d'Hebron Institute of Oncology (VHIO), Universitat Autònoma de Barcelona, Barcelona, Spain.,Clinical Oncology Program, Vall d'Hebron Institute of Oncology (VHIO), Universitat Autonoma de Barcelona, Barcelona, Spain.,Molecular Oncology Program, Vall d'Hebron Institute of Oncology (VHIO), Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Gema Moreno-Bueno
- Biochemistry Department, Universidad Autónoma de Madrid (UAM), Instituto de Investigaciones Biomédicas "Alberto Sols" (CSIC-UAM), IdiPAZ, Madrid, Spain.,Translational Research Laboratory, MD Anderson Internacional Foundation, Madrid, Spain
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8
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Computational Investigation of Homologous Recombination DNA Repair Deficiency in Sporadic Breast Cancer. Sci Rep 2017; 7:15742. [PMID: 29146938 PMCID: PMC5691048 DOI: 10.1038/s41598-017-16138-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2017] [Accepted: 11/03/2017] [Indexed: 12/17/2022] Open
Abstract
BRCAness has important implications in the management and treatment of patients with breast and ovarian cancer. In this study, we propose a computational framework to measure the BRCAness of breast and ovarian tumor samples based on their gene expression profiles. We define a characteristic profile for BRCAness by comparing gene expression differences between BRCA1/2 mutant familial tumors and sporadic breast cancer tumors while adjusting for relevant clinical factors. With this BRCAness profile, our framework calculates sample-specific BRCA scores, which indicates homologous recombination (HR)-mediated DNA repair pathway activity of samples. We found that in sporadic breast cancer high BRCAness score is associated with aberrant copy number of HR genes rather than somatic mutation and other genomic features. Moreover, we observed significant correlations of BRCA score with genome instability and neoadjuvant chemotherapy. More importantly, BRCA score provides significant prognostic value in both breast and ovarian cancers after considering established clinical variables. In summary, the inferred BRCAness from our framework can be used as a robust biomarker for the prediction of prognosis and treatment response in breast and ovarian cancers.
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9
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Qin T, Huang G, Chi L, Sui S, Song C, Li N, Sun S, Li N, Zhang M, Zhao Z, Li L, Li M. Exceptionally high UBE2C expression is a unique phenomenon in basal-like type breast cancer and is regulated by BRCA1. Biomed Pharmacother 2017; 95:649-655. [PMID: 28881292 DOI: 10.1016/j.biopha.2017.08.095] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2017] [Revised: 08/21/2017] [Accepted: 08/23/2017] [Indexed: 12/20/2022] Open
Abstract
Ubiquitin-conjugating enzyme 2C (UBE2C) is overexpressed in various types of cancer, leading to poor outcomes and drug resistance. UBE2C may also have a critical role in phenotypes associated with poor prognosis in breast cancer; however, the relationship between UBE2C expression and clinical outcome in breast cancer subtypes has not previously been investigated. We firstly analyzed breast cancer patient data and immunohistochemistry of breast cancer patient samples. We demonstrated that UBE2C was associated with poor prognosis in breast cancer, particularly basal-like breast cancer, a subtype with aggressive clinical features. Interestingly, we found that there was a close relationship between the expression of BRCA1 and UBE2C in the MCF-7 and MDA-MB-231 breast cancer cell lines. Upregulation of BRCA1 could inhibit the expression of UBE2C. In cells with BRCA1 silenced down, expression of UBE2C was obviously increased, with a concurrent decrease in cellular sensitivity to doxorubicin. Suppression of UBE2C expression by RNA interference led to decrease the mRNA expressions of BCRP, MRP1 and P-gp in doxorubicin-treated MDA-MB-231 cells. Moreover, treatment with 1μg/ml doxorubicin led to increased expression of UBE2C. The results show high expression of UBE2C is a potential prognostic factor of poor outcome in basal-like breast cancer. Moreover, loss of BRCA1 function results in an increase in UBE2C expression and chemical resistance to doxorubicin in breast cancer cells.
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Affiliation(s)
- Tao Qin
- Department of Pathology, Dalian Medical University, Dalian 116044, Liaoning Province, China.
| | - Gena Huang
- Department of Oncology, Second Hospital of Dalian Medical University, Dalian 116023, Liaoning Province, China.
| | - Liyuan Chi
- Department of Oncology, Second Hospital of Dalian Medical University, Dalian 116023, Liaoning Province, China
| | - Silei Sui
- Department of Oncology, Second Hospital of Dalian Medical University, Dalian 116023, Liaoning Province, China
| | - Chen Song
- Department of Oncology, Second Hospital of Dalian Medical University, Dalian 116023, Liaoning Province, China
| | - Na Li
- Department of Oncology, Second Hospital of Dalian Medical University, Dalian 116023, Liaoning Province, China
| | - Siwen Sun
- Department of Oncology, Second Hospital of Dalian Medical University, Dalian 116023, Liaoning Province, China
| | - Ning Li
- Dalian Medical University, Dalian 116044, Liaoning Province, China
| | - Min Zhang
- Department of Oncology, Pulandian Central Hospital, Dalian 116200, Liaoning Province, China
| | - Zuowei Zhao
- Department of Oncology, Second Hospital of Dalian Medical University, Dalian 116023, Liaoning Province, China; Breast Disease and Reconstruction Center, Breast Cancer Key Lab of Dalian, Second Hospital of Dalian Medical University, Dalian 116023, Liaoning Province, China.
| | - Lianhong Li
- Department of Pathology, Dalian Medical University, Dalian 116044, Liaoning Province, China.
| | - Man Li
- Department of Oncology, Second Hospital of Dalian Medical University, Dalian 116023, Liaoning Province, China.
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10
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Milioli HH, Tishchenko I, Riveros C, Berretta R, Moscato P. Basal-like breast cancer: molecular profiles, clinical features and survival outcomes. BMC Med Genomics 2017; 10:19. [PMID: 28351365 PMCID: PMC5370447 DOI: 10.1186/s12920-017-0250-9] [Citation(s) in RCA: 61] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2016] [Accepted: 03/03/2017] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Basal-like constitutes an important molecular subtype of breast cancer characterised by an aggressive behaviour and a limited therapy response. The outcome of patients within this subtype is, however, divergent. Some individuals show an increased risk of dying in the first five years, and others a long-term survival of over ten years after the diagnosis. In this study, we aim at identifying markers associated with basal-like patients' survival and characterising subgroups with distinct disease outcome. METHODS We explored the genomic and transcriptomic profiles of 351 basal-like samples from the METABRIC and ROCK data sets. Two selection methods, labelled Differential and Survival filters, were employed to determine genes/probes that are differentially expressed in tumour and control samples, and are associated with overall survival. These probes were further used to define molecular subgroups, which vary at the microRNA level and in DNA copy number. RESULTS We identified the expression signature of 80 probes that distinguishes between two basal-like subgroups with distinct clinical features and survival outcomes. Genes included in this list have been mainly linked to cancer immune response, epithelial-mesenchymal transition and cell cycle. In particular, high levels of CXCR6, HCST, C3AR1 and FPR3 were found in Basal I; whereas HJURP, RRP12 and DNMT3B appeared over-expressed in Basal II. These genes exhibited the highest betweenness centrality and node degree values and play a key role in the basal-like breast cancer differentiation. Further molecular analysis revealed 17 miRNAs correlated to the subgroups, including hsa-miR-342-5p, -150, -155, -200c and -17. Additionally, increased percentages of gains/amplifications were detected on chromosomes 1q, 3q, 8q, 10p and 17q, and losses/deletions on 4q, 5q, 8p and X, associated with reduced survival. CONCLUSIONS The proposed signature supports the existence of at least two subgroups of basal-like breast cancers with distinct disease outcome. The identification of patients at a low risk may impact the clinical decisions-making by reducing the prescription of high-dose chemotherapy and, consequently, avoiding adverse effects. The recognition of other aggressive features within this subtype may be also critical for improving individual care and for delineating more effective therapies for patients at high risk.
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Affiliation(s)
- Heloisa H. Milioli
- Centre for Bioinformatics, Biomarker Discovery and Information-Based Medicine, Hunter Medical Research Institute, Lot 1, Kookaburra Circuit, New Lambton Heights, 2305 Australia
- School of Environmental and Life Sciences, The University of Newcastle, University Drive, Callaghan, 2308 Australia
| | - Inna Tishchenko
- Centre for Bioinformatics, Biomarker Discovery and Information-Based Medicine, Hunter Medical Research Institute, Lot 1, Kookaburra Circuit, New Lambton Heights, 2305 Australia
- School of Electrical Engineering and Computer Science, The University of Newcastle, University Drive, Callaghan, 2308 Australia
| | - Carlos Riveros
- CReDITSS Unit, Hunter Medical Research Institute, Lot 1, Kookaburra Circuit, New Lambton Heights, 2305 Australia
| | - Regina Berretta
- Centre for Bioinformatics, Biomarker Discovery and Information-Based Medicine, Hunter Medical Research Institute, Lot 1, Kookaburra Circuit, New Lambton Heights, 2305 Australia
- School of Electrical Engineering and Computer Science, The University of Newcastle, University Drive, Callaghan, 2308 Australia
| | - Pablo Moscato
- Centre for Bioinformatics, Biomarker Discovery and Information-Based Medicine, Hunter Medical Research Institute, Lot 1, Kookaburra Circuit, New Lambton Heights, 2305 Australia
- School of Electrical Engineering and Computer Science, The University of Newcastle, University Drive, Callaghan, 2308 Australia
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11
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Thomas C, Henry W, Cuiffo BG, Collmann AY, Marangoni E, Benhamo V, Bhasin MK, Fan C, Fuhrmann L, Baldwin AS, Perou C, Vincent-Salomon A, Toker A, Karnoub AE. Pentraxin-3 is a PI3K signaling target that promotes stem cell-like traits in basal-like breast cancers. Sci Signal 2017; 10:10/467/eaah4674. [PMID: 28223411 DOI: 10.1126/scisignal.aah4674] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Basal-like breast cancers (BLBCs) exhibit hyperactivation of the phosphoinositide 3-kinase (PI3K) signaling pathway because of the frequent mutational activation of the PIK3CA catalytic subunit and the genetic loss of its negative regulators PTEN (phosphatase and tensin homolog) and INPP4B (inositol polyphosphate-4-phosphatase type II). However, PI3K inhibitors have had limited clinical efficacy in BLBC management because of compensatory amplification of PI3K downstream signaling loops. Therefore, identification of critical PI3K mediators is paramount to the development of effective BLBC therapeutics. Using transcriptomic analysis of activated PIK3CA-expressing BLBC cells, we identified the gene encoding the humoral pattern recognition molecule pentraxin-3 (PTX3) as a critical target of oncogenic PI3K signaling. We found that PTX3 abundance is stimulated, in part, through AKT- and nuclear factor κB (NF-κB)-dependent pathways and that presence of PTX3 is necessary for PI3K-induced stem cell-like traits. We further showed that PTX3 expression is greater in tumor samples from patients with BLBC and that it is prognostic of poor patient survival. Our results thus reveal PTX3 as a newly identified PI3K-regulated biomarker and a potential therapeutic target in BLBC.
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Affiliation(s)
- Clémence Thomas
- Department of Pathology and Cancer Center, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA
| | - Whitney Henry
- Department of Pathology and Cancer Center, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA
| | - Benjamin G Cuiffo
- Department of Pathology and Cancer Center, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA
| | - Anthony Y Collmann
- Department of Pathology and Cancer Center, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA
| | | | | | - Manoj K Bhasin
- Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA
| | - Cheng Fan
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | | | - Albert S Baldwin
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Charles Perou
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | | | - Alex Toker
- Department of Pathology and Cancer Center, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA
| | - Antoine E Karnoub
- Department of Pathology and Cancer Center, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA. .,Harvard Stem Cell Institute, Cambridge, MA 02138, USA.,Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
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12
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Contextual Refinement of Regulatory Targets Reveals Effects on Breast Cancer Prognosis of the Regulome. PLoS Comput Biol 2017; 13:e1005340. [PMID: 28103241 PMCID: PMC5289608 DOI: 10.1371/journal.pcbi.1005340] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2016] [Revised: 02/02/2017] [Accepted: 01/03/2017] [Indexed: 01/12/2023] Open
Abstract
Gene expression regulators, such as transcription factors (TFs) and microRNAs (miRNAs), have varying regulatory targets based on the tissue and physiological state (context) within which they are expressed. While the emergence of regulator-characterizing experiments has inferred the target genes of many regulators across many contexts, methods for transferring regulator target genes across contexts are lacking. Further, regulator target gene lists frequently are not curated or have permissive inclusion criteria, impairing their use. Here, we present a method called iterative Contextual Transcriptional Activity Inference of Regulators (icTAIR) to resolve these issues. icTAIR takes a regulator’s previously-identified target gene list and combines it with gene expression data from a context, quantifying that regulator’s activity for that context. It then calculates the correlation between each listed target gene’s expression and the quantitative score of regulatory activity, removes the uncorrelated genes from the list, and iterates the process until it derives a stable list of refined target genes. To validate and demonstrate icTAIR’s power, we use it to refine the MSigDB c3 database of TF, miRNA and unclassified motif target gene lists for breast cancer. We then use its output for survival analysis with clinicopathological multivariable adjustment in 7 independent breast cancer datasets covering 3,430 patients. We uncover many novel prognostic regulators that were obscured prior to refinement, in particular NFY, and offer a detailed look at the composition and relationships among the breast cancer prognostic regulome. We anticipate icTAIR will be of general use in contextually refining regulator target genes for discoveries across many contexts. The icTAIR algorithm can be downloaded from https://github.com/icTAIR. Gene expression regulators, such as transcription factors and microRNAs, are critical actors in cellular physiology and pathophysiology and act by modulating the expression levels of sets of target genes. Given their significance, numerous experiments have sought to characterize the specific target genes of specific regulators, which in turn has led to regulator target gene list databases. Unfortunately, these lists are plagued by poor curation and validation. Further, all lists suffer from the fundamental issue that regulator targets vary across tissue type and physiological state, or “context”, making them poor for conducting downstream, context-specific analyses. To address this issue, here we present a method called icTAIR that contextually-refines regulator target gene lists. To demonstrate its value, we use icTAIR to take the largest-available database of regulator target gene lists, refine it for the breast cancer context, and use both the pre-refined and refined lists for downstream survival analyses in over 3,400 tumors. We find that icTAIR improves the statistical power of the analyses by multiple orders of magnitude. This in turn lets us map the relational network of breast cancer regulators and identify regulators with prognostic effects even after clinicopathological adjustment. We anticipate icTAIR will be broadly useful in regulator studies.
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13
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Wang J, Ding Q, Fujimori H, Motegi A, Miki Y, Masutani M. Loss of CtIP disturbs homologous recombination repair and sensitizes breast cancer cells to PARP inhibitors. Oncotarget 2016; 7:7701-14. [PMID: 26713604 PMCID: PMC4884948 DOI: 10.18632/oncotarget.6715] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2015] [Accepted: 11/27/2015] [Indexed: 01/12/2023] Open
Abstract
Breast cancer is one of the leading causes of death worldwide, and therefore, new and improved approaches for the treatment of breast cancer are desperately needed. CtIP (RBBP8) is a multifunctional protein that is involved in various cellular functions, including transcription, DNA replication, DNA repair and the G1 and G2 cell cycle checkpoints. CtIP plays an important role in homologous recombination repair by interacting with tumor suppressor protein BRCA1. Here, we analyzed the expression profile of CtIP by data mining using published microarray data sets. We found that CtIP expression is frequently decreased in breast cancer patients, and the patient group with low-expressing CtIP mRNA is associated with a significantly lower survival rate. The knockdown of CtIP in breast cancer MCF7 cells reduced Rad51 foci numbers and enhanced f H2AX foci formation after f-irradiation, suggesting that deficiency of CtIP decreases homologous recombination repair and delays DNA double strand break repair. To explore the effect of CtIP on PARP inhibitor therapy for breast cancer, CtIP-depleted MCF7 cells were treated with PARP inhibitor olaparib (AZD2281) or veliparib (ABT-888). As in BRCA mutated cells, PARP inhibitors showed cytotoxicity to CtIP-depleted cells by preventing cells from repairing DNA damage, leading to decreased cell viability. Further, a xenograft tumor model in mice with MCF7 cells demonstrated significantly increased sensitivity towards PARP inhibition under CtIP deficiency. In summary, this study shows that low level of CtIP expression is associated with poor prognosis in breast cancer, and provides a rationale for establishing CtIP expression as a biomarker of PARP inhibitor response, and consequently offers novel therapeutic options for a significant subset of patients.
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Affiliation(s)
- Junhui Wang
- Division of Chemotherapy and Clinical Cancer Research, National Cancer Center Research Institute, Tokyo 104-0045, Japan.,Department of Molecular Genetics, Division of Medical Genomics, Medical Research Institute, Tokyo Medical and Dental University, Tokyo 113-8510, Japan
| | - Qianshan Ding
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Hiroaki Fujimori
- Division of Chemotherapy and Clinical Cancer Research, National Cancer Center Research Institute, Tokyo 104-0045, Japan.,Department of Frontier Life Sciences, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki 852-8588, Japan
| | - Akira Motegi
- Department of Radiation Genetics, Kyoto University Graduate School of Medicine, Kyoto 606-8501 Japan
| | - Yoshio Miki
- Department of Molecular Genetics, Division of Medical Genomics, Medical Research Institute, Tokyo Medical and Dental University, Tokyo 113-8510, Japan
| | - Mitsuko Masutani
- Division of Chemotherapy and Clinical Cancer Research, National Cancer Center Research Institute, Tokyo 104-0045, Japan.,Department of Frontier Life Sciences, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki 852-8588, Japan
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14
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Wang Y, Mark KMK, Ung MH, Kettenbach A, Miller T, Xu W, Cheng W, Xia T, Cheng C. Application of RNAi-induced gene expression profiles for prognostic prediction in breast cancer. Genome Med 2016; 8:114. [PMID: 27788678 PMCID: PMC5084341 DOI: 10.1186/s13073-016-0363-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2016] [Accepted: 10/10/2016] [Indexed: 12/17/2022] Open
Abstract
Homologous recombination (HR) is the primary pathway for repairing double-strand DNA breaks implicating in the development of cancer. RNAi-based knockdowns of BRCA1 and RAD51 in this pathway have been performed to investigate the resulting transcriptomic profiles. Here we propose a computational framework to utilize these profiles to calculate a score, named RNA-Interference derived Proliferation Score (RIPS), which reflects cell proliferation ability in individual breast tumors. RIPS is predictive of breast cancer classes, prognosis, genome instability, and neoadjuvant chemosensitivity. This framework directly translates the readout of knockdown experiments into potential clinical applications and generates a robust biomarker in breast cancer.
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Affiliation(s)
- Yue Wang
- School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China.,Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Hanover, NH, 03755, USA
| | - Kenneth M K Mark
- Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Hanover, NH, 03755, USA
| | - Matthew H Ung
- Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Hanover, NH, 03755, USA
| | - Arminja Kettenbach
- Norris Cotton Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, NH, 03766, USA.,Department of Biochemistry, Geisel School of Medicine at Dartmouth, Hanover, NH, 03755, USA
| | - Todd Miller
- Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Hanover, NH, 03755, USA.,Norris Cotton Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, NH, 03766, USA
| | - Wei Xu
- School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China
| | - Wenqing Cheng
- School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China
| | - Tian Xia
- School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China.
| | - Chao Cheng
- Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Hanover, NH, 03755, USA. .,Norris Cotton Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, NH, 03766, USA. .,Department of Biomedical Data Sciences, Geisel School of Medicine at Dartmouth, Lebanon, NH, 03766, USA.
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15
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Centromere and kinetochore gene misexpression predicts cancer patient survival and response to radiotherapy and chemotherapy. Nat Commun 2016; 7:12619. [PMID: 27577169 PMCID: PMC5013662 DOI: 10.1038/ncomms12619] [Citation(s) in RCA: 132] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2016] [Accepted: 07/19/2016] [Indexed: 12/31/2022] Open
Abstract
Chromosomal instability (CIN) is a hallmark of cancer that contributes to tumour heterogeneity and other malignant properties. Aberrant centromere and kinetochore function causes CIN through chromosome missegregation, leading to aneuploidy, rearrangements and micronucleus formation. Here we develop a Centromere and kinetochore gene Expression Score (CES) signature that quantifies the centromere and kinetochore gene misexpression in cancers. High CES values correlate with increased levels of genomic instability and several specific adverse tumour properties, and prognosticate poor patient survival for breast and lung cancers, especially early-stage tumours. They also signify high levels of genomic instability that sensitize cancer cells to additional genotoxicity. Thus, the CES signature forecasts patient response to adjuvant chemotherapy or radiotherapy. Our results demonstrate the prognostic and predictive power of the CES, suggest a role for centromere misregulation in cancer progression, and support the idea that tumours with extremely high CIN are less tolerant to specific genotoxic therapies. Centromeres and kinetochores are important in maintaining chromosomal stability. Here, the authors show that overexpression of a subset of centromere and kinetochore genes is associated with chromosomal instability and mutation burden in cancer, and predict patient survival and response to genotoxic therapies.
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16
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Tishchenko I, Milioli HH, Riveros C, Moscato P. Extensive Transcriptomic and Genomic Analysis Provides New Insights about Luminal Breast Cancers. PLoS One 2016; 11:e0158259. [PMID: 27341628 PMCID: PMC4920434 DOI: 10.1371/journal.pone.0158259] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2016] [Accepted: 06/13/2016] [Indexed: 12/19/2022] Open
Abstract
Despite constituting approximately two thirds of all breast cancers, the luminal A and B tumours are poorly classified at both clinical and molecular levels. There are contradictory reports on the nature of these subtypes: some define them as intrinsic entities, others as a continuum. With the aim of addressing these uncertainties and identifying molecular signatures of patients at risk, we conducted a comprehensive transcriptomic and genomic analysis of 2,425 luminal breast cancer samples. Our results indicate that the separation between the molecular luminal A and B subtypes—per definition—is not associated with intrinsic characteristics evident in the differentiation between other subtypes. Moreover, t-SNE and MST-kNN clustering approaches based on 10,000 probes, associated with luminal tumour initiation and/or development, revealed the close connections between luminal A and B tumours, with no evidence of a clear boundary between them. Thus, we considered all luminal tumours as a single heterogeneous group for analysis purposes. We first stratified luminal tumours into two distinct groups by their HER2 gene cluster co-expression: HER2-amplified luminal and ordinary-luminal. The former group is associated with distinct transcriptomic and genomic profiles, and poor prognosis; it comprises approximately 8% of all luminal cases. For the remaining ordinary-luminal tumours we further identified the molecular signature correlated with disease outcomes, exhibiting an approximately continuous gene expression range from low to high risk. Thus, we employed four virtual quantiles to segregate the groups of patients. The clinico-pathological characteristics and ratios of genomic aberrations are concordant with the variations in gene expression profiles, hinting at a progressive staging. The comparison with the current separation into luminal A and B subtypes revealed a substantially improved survival stratification. Concluding, we suggest a review of the definition of luminal A and B subtypes. A proposition for a revisited delineation is provided in this study.
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Affiliation(s)
- Inna Tishchenko
- Information-based Medicine Program, Hunter Medical Research Institute, New Lambton Heights, NSW, Australia
- School of Electrical Engineering and Computer Science, The University of Newcastle, Callaghan, NSW, Australia
| | - Heloisa Helena Milioli
- Information-based Medicine Program, Hunter Medical Research Institute, New Lambton Heights, NSW, Australia
- School of Environmental and Life Science, The University of Newcastle, Callaghan, NSW, Australia
| | - Carlos Riveros
- CReDITSS Unit, Hunter Medical Research Institute, New Lambton Heights, NSW, Australia
| | - Pablo Moscato
- Information-based Medicine Program, Hunter Medical Research Institute, New Lambton Heights, NSW, Australia
- School of Electrical Engineering and Computer Science, The University of Newcastle, Callaghan, NSW, Australia
- * E-mail:
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17
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Varn FS, Andrews EH, Mullins DW, Cheng C. Integrative analysis of breast cancer reveals prognostic haematopoietic activity and patient-specific immune response profiles. Nat Commun 2016; 7:10248. [PMID: 26725977 PMCID: PMC4725766 DOI: 10.1038/ncomms10248] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2015] [Accepted: 11/17/2015] [Indexed: 12/17/2022] Open
Abstract
Transcriptional programmes active in haematopoietic cells enable a variety of functions including dedifferentiation, innate immunity and adaptive immunity. Understanding how these programmes function in the context of cancer can provide valuable insights into host immune response, cancer severity and potential therapy response. Here we present a method that uses the transcriptomes of over 200 murine haematopoietic cells, to infer the lineage-specific haematopoietic activity present in human breast tumours. Correlating this activity with patient survival and tumour purity reveals that the transcriptional programmes of many cell types influence patient prognosis and are found in environments of high lymphocytic infiltration. Collectively, these results allow for a detailed and personalized assessment of the patient immune response to a tumour. When combined with routinely collected patient biopsy genomic data, this method can enable a richer understanding of the complex interplay between the host immune system and cancer.
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Affiliation(s)
- Frederick S Varn
- Department of Genetics, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire 03755, USA
| | - Erik H Andrews
- Department of Genetics, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire 03755, USA
| | - David W Mullins
- Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire 03766, USA.,Norris Cotton Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire 03766, USA
| | - Chao Cheng
- Department of Genetics, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire 03755, USA.,Norris Cotton Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire 03766, USA.,Institute for Quantitative Biomedical Sciences, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire 03766, USA
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18
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Milioli HH, Vimieiro R, Riveros C, Tishchenko I, Berretta R, Moscato P. The Discovery of Novel Biomarkers Improves Breast Cancer Intrinsic Subtype Prediction and Reconciles the Labels in the METABRIC Data Set. PLoS One 2015; 10:e0129711. [PMID: 26132585 PMCID: PMC4488510 DOI: 10.1371/journal.pone.0129711] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2014] [Accepted: 05/12/2015] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND The prediction of breast cancer intrinsic subtypes has been introduced as a valuable strategy to determine patient diagnosis and prognosis, and therapy response. The PAM50 method, based on the expression levels of 50 genes, uses a single sample predictor model to assign subtype labels to samples. Intrinsic errors reported within this assay demonstrate the challenge of identifying and understanding the breast cancer groups. In this study, we aim to: a) identify novel biomarkers for subtype individuation by exploring the competence of a newly proposed method named CM1 score, and b) apply an ensemble learning, as opposed to the use of a single classifier, for sample subtype assignment. The overarching objective is to improve class prediction. METHODS AND FINDINGS The microarray transcriptome data sets used in this study are: the METABRIC breast cancer data recorded for over 2000 patients, and the public integrated source from ROCK database with 1570 samples. We first computed the CM1 score to identify the probes with highly discriminative patterns of expression across samples of each intrinsic subtype. We further assessed the ability of 42 selected probes on assigning correct subtype labels using 24 different classifiers from the Weka software suite. For comparison, the same method was applied on the list of 50 genes from the PAM50 method. CONCLUSIONS The CM1 score portrayed 30 novel biomarkers for predicting breast cancer subtypes, with the confirmation of the role of 12 well-established genes. Intrinsic subtypes assigned using the CM1 list and the ensemble of classifiers are more consistent and homogeneous than the original PAM50 labels. The new subtypes show accurate distributions of current clinical markers ER, PR and HER2, and survival curves in the METABRIC and ROCK data sets. Remarkably, the paradoxical attribution of the original labels reinforces the limitations of employing a single sample classifiers to predict breast cancer intrinsic subtypes.
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Affiliation(s)
- Heloisa Helena Milioli
- Priority Research Centre for Bioinformatics, Biomarker Discovery and Information-Based Medicine, Hunter Medical Research Institute, New Lambton Heights, NSW, Australia
- School of Environmental and Life Science, The University of Newcastle, Callaghan, NSW, Australia
| | - Renato Vimieiro
- Priority Research Centre for Bioinformatics, Biomarker Discovery and Information-Based Medicine, Hunter Medical Research Institute, New Lambton Heights, NSW, Australia
- Centro de Informática, Universidade Federal de Pernambuco, Recife, PE, Brazil
| | - Carlos Riveros
- Priority Research Centre for Bioinformatics, Biomarker Discovery and Information-Based Medicine, Hunter Medical Research Institute, New Lambton Heights, NSW, Australia
- School of Electrical Engineering and Computer Science, The University of Newcastle, Callaghan, NSW, Australia
| | - Inna Tishchenko
- Priority Research Centre for Bioinformatics, Biomarker Discovery and Information-Based Medicine, Hunter Medical Research Institute, New Lambton Heights, NSW, Australia
- School of Electrical Engineering and Computer Science, The University of Newcastle, Callaghan, NSW, Australia
| | - Regina Berretta
- Priority Research Centre for Bioinformatics, Biomarker Discovery and Information-Based Medicine, Hunter Medical Research Institute, New Lambton Heights, NSW, Australia
- School of Electrical Engineering and Computer Science, The University of Newcastle, Callaghan, NSW, Australia
| | - Pablo Moscato
- Priority Research Centre for Bioinformatics, Biomarker Discovery and Information-Based Medicine, Hunter Medical Research Institute, New Lambton Heights, NSW, Australia
- School of Electrical Engineering and Computer Science, The University of Newcastle, Callaghan, NSW, Australia
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19
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Ung MH, Varn FS, Cheng C. IDEA: Integrated Drug Expression Analysis-Integration of Gene Expression and Clinical Data for the Identification of Therapeutic Candidates. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2015; 4:415-25. [PMID: 26312165 PMCID: PMC4544055 DOI: 10.1002/psp4.51] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/08/2015] [Accepted: 04/30/2015] [Indexed: 12/14/2022]
Abstract
Cancer drug discovery is an involved process spanning efforts from several fields of study and typically requires years of research and development. However, the advent of high-throughput genomic technologies has allowed for the use of in silico, genomics-based methods to screen drug libraries and accelerate drug discovery. Here we present a novel approach to computationally identify drug candidates for the treatment of breast cancer. In particular, we developed a Drug Regulatory Score similarity metric to evaluate gene expression profile similarity, in the context of drug treatment, and incorporated time-to-event patient survival information to develop an integrated analysis pipeline: Integrated Drug Expression Analysis (IDEA). We were able to predict drug candidates that have been known and those that have not been known in the literature to exhibit anticancer effects. Overall, our method enables quick preclinical screening of drug candidates for breast cancer and other diseases by using the most important indicator of drug efficacy: survival.
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Affiliation(s)
- M H Ung
- Department of Genetics, Geisel School of Medicine at Dartmouth Hanover, New Hampshire, USA
| | - F S Varn
- Department of Genetics, Geisel School of Medicine at Dartmouth Hanover, New Hampshire, USA
| | - C Cheng
- Department of Genetics, Geisel School of Medicine at Dartmouth Hanover, New Hampshire, USA ; Institute for Quantitative Biomedical Sciences, Geisel School of Medicine at Dartmouth Lebanon, New Hampshire, USA ; Norris Cotton Cancer Center, Geisel School of Medicine at Dartmouth Lebanon, New Hampshire, USA
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20
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Varn FS, Ung MH, Lou SK, Cheng C. Integrative analysis of survival-associated gene sets in breast cancer. BMC Med Genomics 2015; 8:11. [PMID: 25881247 PMCID: PMC4359519 DOI: 10.1186/s12920-015-0086-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2014] [Accepted: 02/24/2015] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Patient gene expression information has recently become a clinical feature used to evaluate breast cancer prognosis. The emergence of prognostic gene sets that take advantage of these data has led to a rich library of information that can be used to characterize the molecular nature of a patient's cancer. Identifying robust gene sets that are consistently predictive of a patient's clinical outcome has become one of the main challenges in the field. METHODS We inputted our previously established BASE algorithm with patient gene expression data and gene sets from MSigDB to develop the gene set activity score (GSAS), a metric that quantitatively assesses a gene set's activity level in a given patient. We utilized this metric, along with patient time-to-event data, to perform survival analyses to identify the gene sets that were significantly correlated with patient survival. We then performed cross-dataset analyses to identify robust prognostic gene sets and to classify patients by metastasis status. Additionally, we created a gene set network based on component gene overlap to explore the relationship between gene sets derived from MSigDB. We developed a novel gene set based on this network's topology and applied the GSAS metric to characterize its role in patient survival. RESULTS Using the GSAS metric, we identified 120 gene sets that were significantly associated with patient survival in all datasets tested. The gene overlap network analysis yielded a novel gene set enriched in genes shared by the robustly predictive gene sets. This gene set was highly correlated to patient survival when used alone. Most interestingly, removal of the genes in this gene set from the gene pool on MSigDB resulted in a large reduction in the number of predictive gene sets, suggesting a prominent role for these genes in breast cancer progression. CONCLUSIONS The GSAS metric provided a useful medium by which we systematically investigated how gene sets from MSigDB relate to breast cancer patient survival. We used this metric to identify predictive gene sets and to construct a novel gene set containing genes heavily involved in cancer progression.
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Affiliation(s)
- Frederick S Varn
- Department of Genetics, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, 03755, USA.
| | - Matthew H Ung
- Department of Genetics, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, 03755, USA.
| | - Shao Ke Lou
- Department of Genetics, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, 03755, USA.
| | - Chao Cheng
- Department of Genetics, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, 03755, USA. .,Institute for Quantitative Biomedical Sciences, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire, 03766, USA. .,Norris Cotton Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire, 03766, USA.
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Khaleel SS, Andrews EH, Ung M, DiRenzo J, Cheng C. E2F4 regulatory program predicts patient survival prognosis in breast cancer. Breast Cancer Res 2014; 16:486. [PMID: 25440089 PMCID: PMC4303196 DOI: 10.1186/s13058-014-0486-7] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2013] [Accepted: 11/18/2014] [Indexed: 11/10/2022] Open
Abstract
INTRODUCTION Genetic and molecular signatures have been incorporated into cancer prognosis prediction and treatment decisions with good success over the past decade. Clinically, these signatures are usually used in early-stage cancers to evaluate whether they require adjuvant therapy following surgical resection. A molecular signature that is prognostic across more clinical contexts would be a useful addition to current signatures. METHODS We defined a signature for the ubiquitous tissue factor, E2F4, based on its shared target genes in multiple tissues. These target genes were identified by chromatin immunoprecipitation sequencing (ChIP-seq) experiments using a probabilistic method. We then computationally calculated the regulatory activity score (RAS) of E2F4 in cancer tissues, and examined how E2F4 RAS correlates with patient survival. RESULTS Genes in our E2F4 signature were 21-fold more likely to be correlated with breast cancer patient survival time compared to randomly selected genes. Using eight independent breast cancer datasets containing over 1,900 unique samples, we stratified patients into low and high E2F4 RAS groups. E2F4 activity stratification was highly predictive of patient outcome, and our results remained robust even when controlling for many factors including patient age, tumor size, grade, estrogen receptor (ER) status, lymph node (LN) status, whether the patient received adjuvant therapy, and the patient's other prognostic indices such as Adjuvant! and the Nottingham Prognostic Index scores. Furthermore, the fractions of samples with positive E2F4 RAS vary in different intrinsic breast cancer subtypes, consistent with the different survival profiles of these subtypes. CONCLUSIONS We defined a prognostic signature, the E2F4 regulatory activity score, and showed it to be significantly predictive of patient outcome in breast cancer regardless of treatment status and the states of many other clinicopathological variables. It can be used in conjunction with other breast cancer classification methods such as Oncotype DX to improve clinical outcome prediction.
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Affiliation(s)
- Sari S Khaleel
- Department of Genetics, Geisel School of Medicine at Dartmouth, 1 Rope Ferry Road, Hanover, NH, 03755, USA.
| | - Erik H Andrews
- Department of Genetics, Geisel School of Medicine at Dartmouth, 1 Rope Ferry Road, Hanover, NH, 03755, USA.
| | - Matthew Ung
- Department of Genetics, Geisel School of Medicine at Dartmouth, 1 Rope Ferry Road, Hanover, NH, 03755, USA.
| | - James DiRenzo
- Department of Pharmacology & Toxicology, Geisel School of Medicine at Dartmouth, 1 Rope Ferry Road, Hanover, NH, 03755, USA.
| | - Chao Cheng
- Department of Genetics, Geisel School of Medicine at Dartmouth, 1 Rope Ferry Road, Hanover, NH, 03755, USA.
- Institute for Quantitative Biomedical Sciences, Geisel School of Medicine at Dartmouth, One Medical Center Drive, Lebanon, NH, 03766, USA.
- Norris Cotton Cancer Center, Geisel School of Medicine at Dartmouth, One Medical Center Drive, Lebanon, NH, 03766, USA.
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Oosterkamp HM, Hijmans EM, Brummelkamp TR, Canisius S, Wessels LFA, Zwart W, Bernards R. USP9X downregulation renders breast cancer cells resistant to tamoxifen. Cancer Res 2014; 74:3810-20. [PMID: 25028367 DOI: 10.1158/0008-5472.can-13-1960] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Tamoxifen is one of the most widely used endocrine agents for the treatment of estrogen receptor α (ERα)-positive breast cancer. Although effective in most patients, resistance to tamoxifen is a clinically significant problem and the mechanisms responsible remain elusive. To address this problem, we performed a large scale loss-of-function genetic screen in ZR-75-1 luminal breast cancer cells to identify candidate resistance genes. In this manner, we found that loss of function in the deubiquitinase USP9X prevented proliferation arrest by tamoxifen, but not by the ER downregulator fulvestrant. RNAi-mediated attenuation of USP9X was sufficient to stabilize ERα on chromatin in the presence of tamoxifen, causing a global tamoxifen-driven activation of ERα-responsive genes. Using a gene signature defined by their differential expression after USP9X attenuation in the presence of tamoxifen, we were able to define patients with ERα-positive breast cancer experiencing a poor outcome after adjuvant treatment with tamoxifen. The signature was specific in its lack of correlation with survival in patients with breast cancer who did not receive endocrine therapy. Overall, our findings identify a gene signature as a candidate biomarker of response to tamoxifen in breast cancer.
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Affiliation(s)
- Hendrika M Oosterkamp
- Authors' Affiliations: Division of Molecular Carcinogenesis and Cancer Genomics Center Netherlands; and
| | - E Marielle Hijmans
- Authors' Affiliations: Division of Molecular Carcinogenesis and Cancer Genomics Center Netherlands; and
| | - Thijn R Brummelkamp
- Authors' Affiliations: Division of Molecular Carcinogenesis and Cancer Genomics Center Netherlands; and
| | - Sander Canisius
- Authors' Affiliations: Division of Molecular Carcinogenesis and Cancer Genomics Center Netherlands; and
| | - Lodewyk F A Wessels
- Authors' Affiliations: Division of Molecular Carcinogenesis and Cancer Genomics Center Netherlands; and
| | - Wilbert Zwart
- Division of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - René Bernards
- Authors' Affiliations: Division of Molecular Carcinogenesis and Cancer Genomics Center Netherlands; and
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Zhou F, Li F, Xie F, Zhang Z, Huang H, Zhang L. TRAF4 mediates activation of TGF-β signaling and is a biomarker for oncogenesis in breast cancer. SCIENCE CHINA-LIFE SCIENCES 2014; 57:1172-6. [DOI: 10.1007/s11427-014-4727-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2013] [Accepted: 02/20/2014] [Indexed: 01/25/2023]
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CCNB1 is a prognostic biomarker for ER+ breast cancer. Med Hypotheses 2014; 83:359-64. [DOI: 10.1016/j.mehy.2014.06.013] [Citation(s) in RCA: 81] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2014] [Revised: 06/08/2014] [Accepted: 06/15/2014] [Indexed: 12/15/2022]
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Zhang C, Han Y, Huang H, Qu L, Shou C. High NR2F2 transcript level is associated with increased survival and its expression inhibits TGF-β-dependent epithelial-mesenchymal transition in breast cancer. Breast Cancer Res Treat 2014; 147:265-81. [DOI: 10.1007/s10549-014-3095-3] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2014] [Accepted: 08/06/2014] [Indexed: 01/07/2023]
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Zhang C, Han Y, Huang H, Min L, Qu L, Shou C. Integrated analysis of expression profiling data identifies three genes in correlation with poor prognosis of triple-negative breast cancer. Int J Oncol 2014; 44:2025-33. [PMID: 24676531 DOI: 10.3892/ijo.2014.2352] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2014] [Accepted: 02/27/2014] [Indexed: 11/06/2022] Open
Abstract
Triple-negative breast cancer (TNBC) shows more aggressive clinical behavior and poorer outcome than non-triple-negative breast cancer (NTNBC), and cannot be treated either via endocrine therapy or by Trastuzumab. For TNBC, chemotherapy is currently the mainstay of systemic medical treatment, the lack of more efficient options of treatment has been a problem in breast cancer prevention. In this study, we aimed to find genes related to prognosis in TNBC by bioinformatic analysis and to provide therapeutic candidates for TNBC treatment. We compared the differences in gene expression levels between cancer patients and healthy individuals across five breast cancer microarray databases to generate a gene cohort specifically upregulated in the NTNBC subtype, whose expression levels are ≥2-fold higher in TNBC compared to NTNBC and healthy individuals. Another two databases with clinical information were applied for following Kaplan-Meier analysis, and high expression of BIRC5, CENPA and FAM64A in this cohort were found to be related to poor survival (OS, DMFS, DFS and RFS). This correlation was also seen in patients at early stages and grades. On the other hand, the outcome of patients with synchronous upregulation of these three genes was the worst, while those with synchronous low gene level was the best. In conclusion, BIRC5, CENPA and FAM64A are specifically upregulated in TNBC, and the high expression of these three genes is associated with poor breast cancer prognosis, suggesting their clinical implication as therapeutic targets in TNBC.
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Affiliation(s)
- Cheng Zhang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Biochemistry and Molecular Biology, Peking University Cancer Hospital and Institute, Beijing 100142, P.R. China
| | - Yong Han
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Biochemistry and Molecular Biology, Peking University Cancer Hospital and Institute, Beijing 100142, P.R. China
| | - Hao Huang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Biochemistry and Molecular Biology, Peking University Cancer Hospital and Institute, Beijing 100142, P.R. China
| | - Li Min
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Biochemistry and Molecular Biology, Peking University Cancer Hospital and Institute, Beijing 100142, P.R. China
| | - Like Qu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Biochemistry and Molecular Biology, Peking University Cancer Hospital and Institute, Beijing 100142, P.R. China
| | - Chengchao Shou
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Biochemistry and Molecular Biology, Peking University Cancer Hospital and Institute, Beijing 100142, P.R. China
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Gao Q, Patani N, Dunbier AK, Ghazoui Z, Zvelebil M, Martin LA, Dowsett M. Effect of aromatase inhibition on functional gene modules in estrogen receptor-positive breast cancer and their relationship with antiproliferative response. Clin Cancer Res 2014; 20:2485-94. [PMID: 24634384 DOI: 10.1158/1078-0432.ccr-13-2602] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
PURPOSE To investigate potential associations between gene modules representing key biologic processes and response to aromatase inhibitors (AI) in estrogen receptor-positive (ER(+)) breast cancer. PATIENTS AND METHODS Paired gene expression and Ki67 protein expression were available from 69 postmenopausal women with ER(+) early breast cancer, at baseline and 2 weeks post-anastrozole treatment, in the presurgical setting. Functional gene modules (n = 26) were retrieved from published studies and their module scores were computed before and after elimination of proliferation-associated genes (PAG). Ki67 and module scores were assessed at baseline and 2 weeks post-anastrozole. Unsupervised clustering was used to assess associations between modules and Ki67. RESULTS Proliferation-based modules were highly correlated with Ki67 expression both pretreatment and on-treatment. At baseline with and without PAGs, Ki67 expression was significantly inversely correlated with ERG, ESR1.2, SET, and PIK3CA modules. Modules measuring estrogen signaling strongly predicted antiproliferative response to therapy with and without PAGs. Baseline expression of insulin-like growth factor-1 (IGF-I) module predicted a poor change in Ki67-implicating genes within the module as involved in de novo resistance to AIs. High expression of Immune.2.STAT1 module pretreatment predicted poor antiproliferative response to therapy. A significant association between estrogen-regulated genes modules (ESR1, ESR1-2, SET, and ERG) was evident post AI. CONCLUSIONS Multiple processes and pathways are affected by AI treatment in ER(+) breast cancer. Modules closely associated with ESR1 expression were predictive of good antiproliferative response to AIs, but modules representing immune activity and IGF-I/MAPK were predictive of poor Ki67 response, supporting their therapeutic targeting in combination with AIs.
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Affiliation(s)
- Qiong Gao
- Authors' Affiliations: Breakthrough Breast Cancer Research Centre, Institute of Cancer Research; Academic Department of Biochemistry, Royal Marsden Foundation Trust, London, United Kingdom; and Department of Biochemistry, University of Otago, Dunedin, New Zealand
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CCNA2 is a prognostic biomarker for ER+ breast cancer and tamoxifen resistance. PLoS One 2014; 9:e91771. [PMID: 24622579 PMCID: PMC3951414 DOI: 10.1371/journal.pone.0091771] [Citation(s) in RCA: 93] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2013] [Accepted: 02/13/2014] [Indexed: 11/30/2022] Open
Abstract
Identification of effective prognostic biomarkers and targets are of crucial importance to the management of estrogen receptor positive (ER+) breast cancer. CCNA2 (also known as CyclinA2) belongs to the highly conserved cyclin family and is significantly overexpressed in various cancer types. In this study, we demonstrated that CCNA2 had significant predictive power in distant metastasis free survival, disease free survival, recurrence free survival and overall survival of ER+ breast cancer patients. We also found that CCNA2 was closely associated with tamoxifen resistance. In addition, gene set enrichment analysis (GSEA) revealed that its expression was positively associated with genes overexpressed in endocrine therapy resistant samples. Finally, though CCNA2-Drug interaction network, we demonstrated the interactions between CCNA2 and several available cancer drugs. Overall, we suggest that CCNA2 is a biomarker for the prognosis of ER+ breast cancer and monitoring of tamoxifen efficacy. It's also a promising target for developing new strategies to prevent or even reverse tamoxifen resistance. Moreover, CCNA2 expression may help monitoring tamoxifen efficacy and directing personalized therapies. Nevertheless, in vivo and in vitro experiments and multi-center randomized controlled clinical trials are still needed before its application in clinical settings.
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